Lessons from deploying NLG technology for marine weather forecast text generation

Gowri Somayajulu Sripada; Ehud Baruch Reiter; I  Davy; K  Nilssen

Lessons from deploying NLG technology for marine weather forecast text generation

Gowri Somayajulu Sripada, Ehud Baruch Reiter, I Davy, K Nilssen

Computing Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Citations (Scopus)

Abstract

SUMTIME-MOUSAM is a Natural Language Generation (NLG) system that produces textual weather forecasts for offshore oilrigs from Numerical Weather Prediction (NWT) data. It has been used for the past year by Weathernews (UK) Ltd for producing 150 draft forecasts per day, which are then post-edited by forecasters before being released to end-users. In this paper, we describe how the system works, how it is used at Weathernews and finally some lessons we learnt from building, installing and maintaining SUMTIME-MOUSAM. One important lesson has been that using NLG technology improves maintainability although the biggest maintenance work actually involved changing data formats at the I/O interfaces. We also found our system being used by forecasters in unexpected ways for understanding and editing data. We conclude that the success of a technology owes as much to its functional superiority as to its suitability to the various stakeholders such as developers and users.

Original language	English
Title of host publication	ECAI 2004
Subtitle of host publication	Proceedings of the 16th Eureopean Conference on Artificial Intelligence
Editors	Ramon López de Mántaras, Lorenza Saitta
Place of Publication	Amsterdam, Netherlands
Publisher	IOS Press
Pages	760-764
Number of pages	5
Volume	110
ISBN (Print)	1586034529, 978-1586034528
Publication status	Published - 1 Dec 2004
Event	16th European Conference on Artificial Intelligence (ECAI 2004) including Prestigious Applicants of Intelligent Systems (PAIS 2004) - Valencia, Spain Duration: 22 Aug 2004 → 27 Aug 2004

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Publisher	IOS Press
Volume	110
ISSN (Print)	0922-6389

Conference

Conference	16th European Conference on Artificial Intelligence (ECAI 2004) including Prestigious Applicants of Intelligent Systems (PAIS 2004)
Country	Spain
City	Valencia
Period	22/08/04 → 27/08/04

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

Cite this

Sripada, G. S., Reiter, E. B., Davy, I., & Nilssen, K. (2004). Lessons from deploying NLG technology for marine weather forecast text generation. In R. L. de Mántaras, & L. Saitta (Eds.), ECAI 2004: Proceedings of the 16th Eureopean Conference on Artificial Intelligence (Vol. 110, pp. 760-764). (Frontiers in Artificial Intelligence and Applications; Vol. 110). IOS Press.

Lessons from deploying NLG technology for marine weather forecast text generation. / Sripada, Gowri Somayajulu ; Reiter, Ehud Baruch; Davy, I ; Nilssen, K .

ECAI 2004: Proceedings of the 16th Eureopean Conference on Artificial Intelligence. ed. / Ramon López de Mántaras; Lorenza Saitta. Vol. 110 Amsterdam, Netherlands : IOS Press, 2004. p. 760-764 (Frontiers in Artificial Intelligence and Applications; Vol. 110).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sripada, GS , Reiter, EB, Davy, I & Nilssen, K 2004, Lessons from deploying NLG technology for marine weather forecast text generation. in RL de Mántaras & L Saitta (eds), ECAI 2004: Proceedings of the 16th Eureopean Conference on Artificial Intelligence. vol. 110, Frontiers in Artificial Intelligence and Applications, vol. 110, IOS Press, Amsterdam, Netherlands, pp. 760-764, 16th European Conference on Artificial Intelligence (ECAI 2004) including Prestigious Applicants of Intelligent Systems (PAIS 2004), Valencia, Spain, 22/08/04.

Sripada, Gowri Somayajulu ; Reiter, Ehud Baruch ; Davy, I ; Nilssen, K . / Lessons from deploying NLG technology for marine weather forecast text generation. ECAI 2004: Proceedings of the 16th Eureopean Conference on Artificial Intelligence. editor / Ramon López de Mántaras ; Lorenza Saitta. Vol. 110 Amsterdam, Netherlands : IOS Press, 2004. pp. 760-764 (Frontiers in Artificial Intelligence and Applications).

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abstract = "SUMTIME-MOUSAM is a Natural Language Generation (NLG) system that produces textual weather forecasts for offshore oilrigs from Numerical Weather Prediction (NWT) data. It has been used for the past year by Weathernews (UK) Ltd for producing 150 draft forecasts per day, which are then post-edited by forecasters before being released to end-users. In this paper, we describe how the system works, how it is used at Weathernews and finally some lessons we learnt from building, installing and maintaining SUMTIME-MOUSAM. One important lesson has been that using NLG technology improves maintainability although the biggest maintenance work actually involved changing data formats at the I/O interfaces. We also found our system being used by forecasters in unexpected ways for understanding and editing data. We conclude that the success of a technology owes as much to its functional superiority as to its suitability to the various stakeholders such as developers and users.",

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